The corresponding quantitative comparison results are shown in Fig. REF , we observe that 1). Our proposed video coding framework significantly outperforms the exiting learning-based video compression methods in both PSNR and MS-SSIM. 2). As an end-to-end learning-based codec, our coding framework outperforms the most mainstream traditional hybrid coding framework–H.266 [VTM-8.0] in terms of MS-SSIM. 3). Compared to H.265 [HM-16.21], our codec that is optimized with MSE provides competitive results in PSNR but achieves better results in MS-SSIM. We analyse that is because the autoencoder-based compression modules are easy to train with MS-SSMI as an objective, and the same trend has been observed in other learning-based codecs [1]}, [2]}.
